ABSTRACT A portion of the genetic component of Alzheimer disease (AD) is explained by genes identified by positional cloning, targeted gene analysis, GWAS and next generation sequencing approaches. With few notable exceptions, the functional variants in these genes and precise pathogenic mechanisms by which these variants lead to AD are unknown. We will continue to direct our efforts on persons of African ancestry (AA), a group with a high incidence of dementia but studied much less than persons of European ancestry (EA). We will leverage rich AD-related endophenotype and other risk factor data from the largest collection of AAs assembled by us, the Alzheimer Disease Genetics Consortium (ADGC) and Alzheimer Disease Sequencing Project (ADSP) for genetic studies of AD to promote further discovery of AD-related genes and variants as well as their mechanisms of action leading to AD. Previously, we demonstrated significant association of AD with SORL1, AKAP9, and other genes in AAs using standard and novel analytic approaches. In the next project period, we will perform RNA sequencing on brain tissue obtained from more than 140 AA AD cases and controls, and analyze these data using state-of-the-art bioinformatics approaches to assess the influence of AD risk variants on gene expression. We will construct AA-specific Bayesian elastic-net models of genetically- mediated gene expression using the AA brain cohort genotype and RNAseq data. These models will be applied to AA cohorts from the ADGC and ADSP (total n=9,200) using PrediXcan to construct AA-specific expression predictions In addition, we will identify non-genetic mediators of genetic influences on AD risk by (1) performing gene ? environment GWASs of AD in AAs using data for several established AD risk factors using data from the ADGC/ADSP, UK Biobank and Million Veterans Program; (2) applying mendelian randomization to assess the causal relationship between diabetes, cigarette smoking, hypertension, hypercholesterolemia, obesity and AD in AAs using existing GWAS summary statistics for these traits in AAs; and (3) conducting a phenome-wide association study (PheWAS) to identify pleiotropy by deriving a polygenic risk score for AD in AAs and testing its association with a range of phenotypes within the UK Biobank. We will also perform in vitro experiments (including knockdown by siRNA, gene and protein over-expression, immunofluorescence, and ELISA) in human neuronal cells and induced pluripotent stem cell-derived neurons containing AA AD risk variants inserted by CRISPR to understand how genetic variation in AKAP9 and other promising genes leads to AD-related pathologic states as well as to provide models that can be used in small molecule drug screens for potential AD treatments.